Selecting discriminative features in social media data: An unsupervised approach

The usage of high-dimensional data complicates data processing in social network area. Accordingly, the researchers are motivated to propose some novel approaches to overcome this challenge. One of the best solutions is extracting the effective information from data pool and discarding the unnecessary ones. Feature selection is a known technique which aims to distinguish the discriminative features. Because of the unlabeled nature of datasets in social network, an unsupervised feature selection algorithm might be a good scenario. In addition to features, we try to confront the inherently linked users in social network datasets. This is because a stronger unsupervised feature selection technique is needed to ignore the independent and identically distributed assumption of data. Hence, by optimizing a novel objective function in this paper, the top-ranked features are extracted for further processing. This objective function incorporates both the inter-relationship of users in addition to their features. An efficient iterative algorithm is also designed to optimize the proposed objective function. We compare our method with two supervised and unsupervised evaluation criteria on real-world social network datasets. The experimental results demonstrate the effectiveness of our proposed approach.